class_docstr: Ensembler that takes the mean of logits returned by its subnetworks.
  
  Attributes:
    name: Optional name for the ensembler. Defaults to 'complexity_regularized'.
    add_mean_last_layer_predictions: Set to True to add mean of last_layer in
      subnetworks in estimator's predictions and export outputs.
class_docstr_tok: ["Ensembler","that","takes","the","mean","of","logits","returned","by","its","subnetworks",".","Attributes",":","name",":","Optional","name","for","the","ensembler",".","Defaults","to","'complexity_regularized","'",".","add_mean_last_layer_predictions",":","Set","to","True","to","add","mean","of","last_layer","in","subnetworks","in","estimator","'s","predictions","and","export","outputs","."]
code: def __init__(self, name=None, add_mean_last_layer_predictions=False):
      self._name = name
      self._add_mean_last_layer_predictions = (
          add_mean_last_layer_predictions
      )
  
file_path: adanet/adanet/ensemble/mean.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from adanet.ensemble.ensembler import Ensemble
  from adanet.ensemble.ensembler import Ensembler
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new MeanEnsembler object from inherited base classes: Ensembler with example: obj = MeanEnsembler(name, add_mean_last_layer_predictions)